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Modeling water diffusion anisotropy within fixed newborn primate brain using Bayesian probability theory
Author(s) -
Kroenke Christopher D.,
Bretthorst G. Larry,
Inder Terrie E.,
Neil Jeffrey J.
Publication year - 2006
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.20728
Subject(s) - diffusion mri , voxel , fractional anisotropy , bayesian probability , computer science , univariate , prior probability , artificial intelligence , pattern recognition (psychology) , magnetic resonance imaging , multivariate statistics , machine learning , medicine , radiology
An active area of research involves optimally modeling brain diffusion MRI data for various applications. In this study Bayesian analysis procedures were used to evaluate three models applied to phase‐sensitive diffusion MRI data obtained from formalin‐fixed perinatal primate brain tissue: conventional diffusion tensor imaging (DTI), a cumulant expansion, and a family of modified DTI expressions. In the latter two cases the optimum expression was selected from the model family for each voxel in the image. The ability of each model to represent the data was evaluated by comparing the magnitude of the residuals to the thermal noise. Consistent with previous findings from other laboratories, the DTI model poorly represented the experimental data. In contrast, the cumulant expansion and modified DTI expressions were both capable of modeling the data to within the noise using six to eight adjustable parameters per voxel. In these cases the model selection results provided a valuable form of image contrast. The successful modeling procedures differ from the conventional DTI model in that they allow the MRI signal to decay to a positive offset. Intuitively, the positive offset can be thought of as spins that are sufficiently restricted to appear immobile over the sampled range of b ‐values. Magn Reson Med, 2006. © 2005 Wiley‐Liss, Inc.